To the Editor:Chest pain is one of the most common complaints for patients attending emergency departments(EDs)globally.It is important to accurately stratify risk of possible acute coronary syndrome(ACS)for these pat...To the Editor:Chest pain is one of the most common complaints for patients attending emergency departments(EDs)globally.It is important to accurately stratify risk of possible acute coronary syndrome(ACS)for these patients.[1]Several risk stratification scores such as thrombolysis in myocardial infarction(TIMI),global registry for acute coronary events(GRACE),Banach and HEART are helpful.[2]Previous research in our setting compared these four scores and found that the HEART score,with a C-statistic of 0.731,was the best for predicting 7-day major adverse cardiac events(MACE)The purpose of this study was to develop risk stratification prediction models for 7-day MACE in patients with chest pain,utilizing machine learning algorithms such as eXtreme Gradient Boosting(XGBoost),Support Vector Machine(SVM).展开更多
基金supported by grants from the Scientific Research Project of the Guangzhou Education Bureau(No.1201610645)the Key Medical Disciplines and Specialties Program of Guangzhou.
文摘To the Editor:Chest pain is one of the most common complaints for patients attending emergency departments(EDs)globally.It is important to accurately stratify risk of possible acute coronary syndrome(ACS)for these patients.[1]Several risk stratification scores such as thrombolysis in myocardial infarction(TIMI),global registry for acute coronary events(GRACE),Banach and HEART are helpful.[2]Previous research in our setting compared these four scores and found that the HEART score,with a C-statistic of 0.731,was the best for predicting 7-day major adverse cardiac events(MACE)The purpose of this study was to develop risk stratification prediction models for 7-day MACE in patients with chest pain,utilizing machine learning algorithms such as eXtreme Gradient Boosting(XGBoost),Support Vector Machine(SVM).
